Application of Computer Vision Techniques for Segregation of PlasticWaste based on Resin Identification Code
It addresses plastic waste segregation for recycling, but the approach is incremental as it applies existing machine learning methods to a specific domain.
This paper tackles the problem of identifying plastic waste by its resin identification code for efficient recycling, achieving 99.74% accuracy for known categories using one-shot learning and 95% accuracy for new categories with dimensionality reduction techniques.
This paper presents methods to identify the plastic waste based on its resin identification code to provide an efficient recycling of post-consumer plastic waste. We propose the design, training and testing of different machine learning techniques to (i) identify a plastic waste that belongs to the known categories of plastic waste when the system is trained and (ii) identify a new plastic waste that do not belong the any known categories of plastic waste while the system is trained. For the first case,we propose the use of one-shot learning techniques using Siamese and Triplet loss networks. Our proposed approach does not require any augmentation to increase the size of the database and achieved a high accuracy of 99.74%. For the second case, we propose the use of supervised and unsupervised dimensionality reduction techniques and achieved an accuracy of 95% to correctly identify a new plastic waste.